A MINLP model for combination pressurization optimization of shale gas gathering system
Tóm tắt
The combination pressurization of the shale gas gathering system is one of the most common pressurization methods in the current engineering site, but it is mostly set by manual experience or simulation analysis, and thus the optimal pressurization scheme cannot be obtained. In order to optimize the pressurization mode of the shale gas gathering and transportation system, a mixed integer nonlinear programming model (MINLP) is established based on the existing pressurization mode of the shale gas field. The model takes the minimum total cost of the compressor unit as the objective function. Various constraints are also taken into account, such as pipe pressure, flowrate, compressor related, well and platform throttling, uniqueness for well and platform pressurization. Solving this optimization model can figure out the appropriate pressurization position, operating power, and compressor unit cost. An actual case for a shale gas block is applied to determine the combined pressurization scheme suitable for this production condition. The results show that the combination of more pressurization methods can meet the pressurization requirements under different production conditions. When both well and platform pressurization are considered, the optimized pressurization position is more concentrated, the number of compressors is reduced by two sets, and the total compressor cost is reduced by 99.28 × 104 Yuan, which reflects the advantages of combined pressurization in the pressurization of shale gas gathering and transportation systems.
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Amosa MK, Aderibigbe FA, Adeniyi AG, Ighalo JO, Bello BT, Jami MS, Alkhatib MFR, Majozi T (2021) Auto-correlation robustness of factorial designs and GAMS in studying the effects of process variables in a dual-objective adsorption system. Appl Water Sci 2(11):43
Caballero JA, Labarta JA, Quirante N, Carrero-Parreño A, Grossmann IE (2020) Environmental and economic water management in shale gas extraction. Sustainability 4(12):1686
Cafaro DC, Grossmann IE (2014) Strategic planning, design, and development of the shale gas supply chain network. AIChE J 60(6):2122–2142
Cafaro DC, Grossmann IE (2021) Optimal design of water pipeline networks for the development of shale gas resources. AIChE J 67(1):17058
Cafaro DC, Drouven MG, Grossmann IE (2018) Continuous-time formulations for the optimal planning of multiple refracture treatments in a shale gas well. AIChE J 64(5):16095
Carrero-Parreño A, Reyes-Labarta JA, Salcedo-Díaz R, Ruiz-Femenia R, Onishi VC, Caballero JA, Grossmann IE (2018) Holistic planning model for sustainable water management in the shale gas industry. Ind Eng Chem Res 57(39):13131–13143
Colin-Robledo J, Martínez-Guido SI, Guerra-González R, Lira-Barragán LF, Ponce-Ortega JM (2019) Economic and environmental assessment of gas supply chains incorporating shale gas. Ind Eng Chem Res 58(41):19122–19134
Das B, Kumar A (2017) Cost optimization of a hybrid energy storage system using GAMS. In: 2017 international conference on power and embedded drive control (ICPEDC), pp 89–92
Drouven MG, Grossmann IE (2016) Multi-period planning, design, and strategic models for long-term, quality-sensitive shale gas development. AIChE J 62(7):2296–2323
Drouven MG, Grossmann IE (2017) Mixed-integer programming models for line pressure optimization in shale gas gathering systems. J Petrol Sci Eng 157:1021–1032
Galan B, Grossmann IE (1998) Optimal design of distributed wastewater treatment networks. Ind Eng Chem Res 37(10):4036–4048
Gao J, You F (2015b) Stochastic programming approach to optimal design and operations of shale gas supply chain under uncertainty. In: IEEE conference on decision and control, pp 6656–6661
Gao J, You F (2015a) Optimal design and operations of supply chain networks for water management in shale gas production: MILFP model and algorithms for the water-energy nexus. AIChE J 61(4):1184–1208
Gregory KB, Vidic RD, Dzombak DA (2011) Water management challenges associated with the production of shale gas by hydraulic fracturing. Elements 7(3):181–186
Guerra OJ, Calderon AJ, Papageorgiou LG, Siirola JJ, Reklaitis GV (2016) An optimization framework for the integration of water management and shale gas supply chain design. Comput Chem Eng 92:230–255
Hong B, Li X, Di G, Li Y, Liu X, Chen S, Gong J (2019) An integrated MILP method for gathering pipeline networks considering hydraulic characteristics. Chem Eng Res Des 152:320–335
Hong B, Li X, Song S, Chen SL, Zhao C, Gong J (2020) Optimal planning and modular infrastructure dynamic allocation for shale gas production. Appl Energy 261:114439
Liu HX, Li JH (2018) The US shale gas revolution and its externality on crude oil prices: a counterfactual analysis. Sustainability 10(3):697
Liu EB, Li CJ, Yang Y (2014) Optimal energy consumption analysis of natural gas pipeline. Sci World J 2014:506138
Liu K, Biegler LT, Zhang BJ, Chen QL (2020) Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty. Chem Eng Sci 215:115449
Liu Q, Mao L, Li FF (2016) An intelligent optimization method for oil-gas gathering and transportation pipeline network layout. In: 2016 Chinese control and decision conference, pp 4621–4626
Loucks RG, Reed RM, Ruppel SC, Jarvie DM (2009) Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett shale. J Sediment Res 79(12):848–861
Montagna AF, Cafaro DC, Grossmann IE, Burch D, Shao Y, Wu XH, Furman K (2021) Pipeline network design for gathering unconventional oil and gas production using mathematical optimization. Optim Eng. https://doi.org/10.1007/s11081-021-09695-z
Montoya OD, Garrido VM, Grisales-Norena LF, Gil-González W, Garces A, Ramos-Paja CA (2018) Optimal location of DGs in DC power grids using a MINLP model implemented in GAMS. In: 2018 instrumentation and measurement meeting (EPIM), pp 1-5
Orejuela Luna VH, Espinosa Gualotuña SR (2018) Optimization of distribution transformers using GAMS. In: 2018 IEEE ANDESCON, pp 1–9
Ren KP, Tang X, Jin Y, Wang JL, Feng CY, Höök M (2019) Bi-objective optimization of water management in shale gas exploration with uncertainty: a case study from Sichuan. China Resour Conserv Recycl 143:226–235
Skworcow P, Paluszczyszyn D, Ulanicki B, Rudek R, Belrain T (2014) Optimisation of pump and valve schedules in complex large-scale water distribution systems using GAMS modelling language. Procedia Eng 70:1566–1574
Tartibu LK, Sun B, Kaunda MAE (2015) Multi-objective optimization of the stack of a thermoacoustic engine using GAMS. Appl Soft Comput 28:30–43
Wang JY (2019) Application and evaluation of variable frequency energy-saving technology for reciprocating compressor in CBM Field. IOP Conf Ser Earth Environ Sci 237(4):42004
Wei LX, Dong H, Zhao J, Zhou G (2016) Optimization model establishment and optimization software development of gas field gathering and transmission pipeline network system. J Intell Fuzzy Syst 31(4):2375–2382
Wei J, Duan HM, Yan Q (2021) Shale gas: will it become a new type of clean energy in China?—a perspective of development potential. J Clean Prod 294:126257
Yang L, Grossmann IE, Manno J (2014) Optimization models for shale gas water management. AIChE J 60(10):3490–3501
Zagorowska M, Skourup C, Thornhill NF (2020) Influence of compressor degradation on optimal operation of a compressor station. Comput Chem Eng 143:107104
Zaro FR, Alqam SJ (2019) Solving dynamic load economic dispatch using GAMS optimization algorithm. In: IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), pp 866–871
Zhang H, Liang Y, Zhang W, Wang B, Yan X, Liao Q (2017) A unified MILP model for topological structure of production well gathering pipeline network. J Pet Sci Eng 152:284–293
Zhou C, Liu P, Pei Z (2014a) A superstructure-based mixed-integer programming approach to optimal design of pipeline network for large-scale CO2 transport. AIChE J 60(7):2442–2461
Zhou J, Gong J, Li XP, Tong T, Cheng MY, Zhang SQ (2014) Optimization of coalbed methane gathering system in China. Adv Mech Eng 6(1):147381